Steganography using Social Impact Theory based Optimization (SITO)

Abstract

With a great advancement in science and technology, efficient techniques are needed for the purpose of security and copyright protection of the digital information being transmitted over the internet and for secret data communication. Thus, Steganography solves this purpose which has been used widely. Even though, a Stego-object may be exposed to noise or compression due to which the secret data cannot be extracted correctly at the receiver's end, when the transmission occours. This paper presents an efficient image hiding scheme, Social Impact theory based Optimization (SITO). Here, a fitness function is computed based on certain texture properties and entropy of a host image. According to this, the block holding the most relevant fitness value is the place where embedding of the secret data (secret image) is done. Thus, a stego-image is retrieved at the other end, which is not only good in quality but is also able to sustain certain noise and compression attacks during the transmission. The objective function is defined in such a manner that both quality and robustness of the stego image are acceptable, for which the performance analysis parameter values of the stego-image are also determined. The results, when compared with some other data hiding technique show better stego image quality along with distortion tolerance.

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